{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-3-7532975c715b>:6: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  ax=plt.subplot(111)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.linspace(-5,5,100)\n",
    "y=np.sin(x)\n",
    "plt.plot(x,y)\n",
    "plt.annotate('deep learning',xy=(x[10],y[10]),xytext=(-2,1),\n",
    "            arrowprops=dict(arrowstyle='->',connectionstyle='arc3,rad=0.3'))\n",
    "\n",
    "ax=plt.subplot(111)\n",
    "plt.xlabel('河南理工大学')\n",
    "ax.spines['right'].set_color('none')\n",
    "ax.spines['top'].set_color('none')\n",
    "ax.spines['bottom'].set_position(('data',0))\n",
    "ax.spines['left'].set_position(('data',0))\n",
    "plt.xticks([-4,-2,0,2,4],[-400000000000,-200000000000,0,200000000000,40000000000],\n",
    "          rotation=60)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}